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| import torch | |
| import torchvision | |
| from torch import nn | |
| def create_resnet50_model(num_classes:int=8, # 4 | |
| seed:int=42): | |
| """Creates an ResNet50 feature extractor model and transforms. | |
| Args: | |
| num_classes (int, optional): number of classes in the classifier head. | |
| Defaults to 3. | |
| seed (int, optional): random seed value. Defaults to 42. | |
| Returns: | |
| model (torch.nn.Module): ResNet50 feature extractor model. | |
| transforms (torchvision.transforms): ResNet50 image transforms. | |
| """ | |
| # 1, 2, 3. Create ResNet50 pretrained weights, transforms and model | |
| weights = torchvision.models.ResNet50_Weights.DEFAULT | |
| transforms = weights.transforms() | |
| model = torchvision.models.resnet50(weights=weights) | |
| # 4. Freeze all layers in base model | |
| for param in model.parameters(): | |
| param.requires_grad = True # Set to False for model's other than ResNet | |
| # 5. Change classifier head with random seed for reproducibility | |
| torch.manual_seed(seed) | |
| model.classifier = nn.Sequential( | |
| nn.Dropout(p=0.3, inplace=True), | |
| nn.Linear(in_features=2048 | |
| , out_features=num_classes), # If using EffnetB2 in_features = 1408, EffnetB0 in_features = 1280, if ResNet50 in_features = 2048 | |
| ) | |
| return model, transforms | |